Late diagnosis of CKD and associated survival after initiation of renal replacement therapy in Kazakhstan: analysis of nationwide electronic healthcare registry 2014–2019
Bibliographic record
Abstract
Chronic kidney disease (CKD) presents a significant global health challenge, often progressing to end-stage renal disease (ESRD) necessitating renal replacement therapy (RRT). Late referral (LR) to nephrologists before RRT initiation is linked with adverse outcomes. However, data on CKD diagnosis and survival post-RRT initiation in Kazakhstan remain limited. This study aims to investigate the impact of late CKD diagnosis on survival prognosis after RRT initiation. Data were acquired from the Unified National Electronic Health System (UNEHS) for CKD patients initiating RRT between 2014 and 2019. Survival post-RRT initiation was assessed using the Cox Proportional Hazards Model. Totally, 211,655 CKD patients were registered in the UNEHS databases and 9,097 (4.3%) needed RRT. The most prevalent age group among RRT patients is 45–64 years, with a higher proportion of males (56%) and Kazakh ethnicity (64%). Seventy-four percent of patients were diagnosed late. The median follow-up time was 537 (IQR: 166–1101) days. Late diagnosis correlated with worse survival (HR = 1.18, p < 0.001). Common comorbidities among RRT patients include hypertension (47%), diabetes (21%), and cardiovascular diseases (26%). The history of transplantation significantly influenced survival. Regional disparities in survival probabilities were observed, highlighting the need for collaborative efforts in healthcare delivery. This study underscores the substantial burden of CKD in Kazakhstan, with a majority of patients diagnosed late. Early detection strategies and timely kidney transplantation emerge as crucial interventions to enhance survival outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".